Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Amy Dunbar <Amy.Dunbar@business.uconn.edu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: RE: st: Robust Standard Errors in Paneldatasets |

Date |
Tue, 26 Oct 2010 15:16:29 +0000 |

Kit, sorry to bother you again, but when I tried to incorporate what you said about time dummies, I realized why I thought the time dummies address cross-section correlation. Petersen wrote (p. 458): One way empirical finance researchers can address two sources of correlation is to parametrically estimate one of the dimensions (e.g., by including dummy variables). Since many panel data sets have more firms than years, a common approach is to include dummy variables for each time period (to absorb the time effect) and then cluster by firm (Lamont and Polk, 2001; Anderson and Reeb, 2004; Gross and Souleles, 2004; Sapienza, 2004; and Faulkender and Petersen, 2006). If the time effect is fixed (e.g., Equation (15)), the time dummies completely remove the correlation between observations in the same time period. In this case, there is only a firm effect left in the data. As seen in Section 1, OLS and Fama-MacBeth standard errors are biased in this case, while standard errors clustered by firm are unbiased (results available from the author). Obviously I am still missing a critical point. Could you help understand your point about time dummies not correcting for cross-sectional correlation? Ant -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Amy Dunbar Sent: Tuesday, October 26, 2010 10:56 AM To: statalist@hsphsun2.harvard.edu Subject: RE: RE: st: Robust Standard Errors in Paneldatasets Thank you, Kit. I have a better understanding of time indicators now. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Christopher Baum Sent: Tuesday, October 26, 2010 10:36 AM To: statalist@hsphsun2.harvard.edu Subject: re: RE: st: Robust Standard Errors in Paneldatasets <> Amy wrote Kit Baum wrote: "None of what you have found deals with clustering." When I followed up on Kit's -xtivreg2_ suggestion, I found the following in the help for ivreg2: cluster(varname1 varname2) provides 2-way cluster-robust SEs and statistics as proposed by Cameron, Gelbach and Miller (2006) and Thompson (2009). "Two-way cluster-robust" means the SEs and statistics are robust to arbitrary within-group correlation in two distinct non-nested categories defined by varname1 and varname2. A typical application would be panel data where one "category" is the panel and the other "category" is time; the resulting SEs are robust to arbitrary within-panel autocorrelation (clustering on panel id) and to arbitrary contemporaneous cross-panel correlation (clustering on time). In Petersen, Mitchell A. 2009. Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches. Review of Financial Studies 22 (1), Petersen provides a link to his web site (http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/se_programming.htm). On his web page he states: "The routines currently written into Stata allow you to cluster by only one variable (e.g. one dimension such as firm or time). Papers by Thompson (2006) and by Cameron, Gelbach and Miller (2006) suggest a way to account for multiple dimensions at the same time. This approach allows for correlations among different firms in the same year and different years in the same firm, for example. See their papers and mine for more details and caveats. I have written a Stata ado file to implement this estimation procedure." The help file above indicates that -ivreg2- does deal with both, so I'm not sure what I am missing. If I'm correct, -ivreg2- came out in 2008, so maybe Petersen wrote his paper before -ivreg2-, but his website doesn't mention -ivreg2-. Also see Gow, I., G. Ormazabal, and D. Taylor. 2010. Correcting for Cross-Sectional and Time-Series Dependence in Accounting Research. The Accounting Review 85 (2):483. This paper references Petersen's Stata code. It's still not clear to me when it's ok to deal with time effects (cross-sectional correlation) parametrically by including a time indicator variable and just correct for time-series dependence (serial correlation) with cluster (firm) or vice versa. The -ivreg2- help states, "Users should be aware of the asymptotic requirements for the consistency of the chosen VCE," so when T is short, is the best option the parametric option? - ivreg2- has a small sample correction option, so when would that be appropriate as opposed to including a time indicator variable? Thank you for considering my question. The Petersen piece was published in 2009, but substantially completed in 2005: http://ideas.repec.org/p/nbr/nberwo/11280.html so it is not surprising that it is not up to date with respect to changes made in -ivreg2-(SSC) in 2008. Including time dummies allows for proper specification of the intercept if indeed it is time-varying. It does nothing to allow for correlation of errors across firms (this for some reason is a common misconception). When you use one-way clustering by panel (which is what xtreg, fe does with -robust-) you are still assuming that the errors are independent across panels. Using the -small- option just adjusts for d.f., and if you have a large panel, the difference between t and z will be negligible. Including time indicators deals with specification; -small- only affects the VCE. If your intercept should be time-varying, omitting time indicators would be a misspecification. Ignoring cross-panel correlation could cause the VCE to be biased, but if T is small, your two-way clustering results may not be that much of an improvement. In my BOS'10 and UKSUG2010 presentations with Mark Schaffer and Austin Nichols, we discuss some of these issues. Kit Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: RE: st: Robust Standard Errors in Paneldatasets***From:*Stas Kolenikov <skolenik@gmail.com>

**References**:**re: RE: st: Robust Standard Errors in Paneldatasets***From:*Christopher Baum <kit.baum@bc.edu>

**RE: RE: st: Robust Standard Errors in Paneldatasets***From:*Amy Dunbar <Amy.Dunbar@business.uconn.edu>

- Prev by Date:
**Re: st: Robust Standard Errors in Paneldatasets** - Next by Date:
**st: predicted values in svy glm l(log) f(poisson)** - Previous by thread:
**Re: st: Robust Standard Errors in Paneldatasets** - Next by thread:
**Re: RE: st: Robust Standard Errors in Paneldatasets** - Index(es):